library(tidyverse)
library(tidycensus)
library(mapview)
library(tmap)
library(sf)
library(tmaptools)
tmap_mode('view')

# B27010_051 65 and over pop
# B27010_001 total pop

vars <- c('B27010_001', 'B27010_051')

us_county_test <- get_acs(geography = "county", year = 2018, variables = vars, 
                            shift_geo = TRUE, geometry = TRUE)

counties <- us_county_test %>% 
  select(-moe) %>% 
  group_by(GEOID) %>% 
  spread(variable, estimate)

counties <- counties %>% 
  mutate(prop_65_above = B27010_051 / B27010_001) %>%
  rename('Geo_FIPS' = 'GEOID',
         'Geo_QNAME' = 'NAME')

health_rank_full <- read_csv('health_county_full_2016.csv')

cols_to_keep <- c('Geo_QNAME', 'Geo_NAME', 'Geo_FIPS', 'ORG_HD2016_001_HEALTH_T6_V25', 'ORG_HD2016_001_HEALTH_T6_V13', 'ORG_HD2016_003_HEALTH_T9_V9', 'ORG_HD2016_006_HEALTH_T3_V42', 'ORG_HD2016_006_HEALTH_T3_V35', 'ORG_HD2016_006_HEALTH_T3_V25')

health_condense <- health_rank_full %>% 
  select(., all_of(cols_to_keep)) %>% 
  rename('Total Population' = 'ORG_HD2016_001_HEALTH_T6_V25',
         'Percent Population 65 and Above' = 'ORG_HD2016_001_HEALTH_T6_V13',
         'Percent Uninsured (Ages 18-64)' = 'ORG_HD2016_003_HEALTH_T9_V9',
         'Percent Without Insurance (Under 65)' = 'ORG_HD2016_006_HEALTH_T3_V42',
         'Primary Care Physicians per 100K' = 'ORG_HD2016_006_HEALTH_T3_V35',
         'Preventable Hospital Stays per 100K Medicare enrollees' = 'ORG_HD2016_006_HEALTH_T3_V25')

health_counties <- merge(counties, health_condense, by = 'Geo_FIPS')

cases_url_march_24 <- 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-24-2020.csv'

# tmp <- tempfile()
# curl_download(url, tmp)

cases <- read_csv(cases_url_march_24)

us_cases <- cases %>% 
  filter(Country_Region == 'US') %>% 
  rename('Geo_FIPS' = 'FIPS',
         'Confirmed Cases' = 'Confirmed')

# length(unique(us_cases$Geo_FIPS))

cases_health_counties <- merge(health_counties, us_cases, by = 'Geo_FIPS')

cases_health_counties <- cases_health_counties %>% 
  mutate('Confirmed Cases per 10K' = `Confirmed Cases` / (`Total Population` / 10000)) %>% 
  mutate('Confirmed Cases per 100K' = `Confirmed Cases` / (`Total Population` / 100000))

shape_reproj <- set_projection(cases_health_counties, projection = 'longlat')

cty_map <- tm_shape(shape_reproj) +
  tm_polygons('Confirmed Cases per 100K', style = 'jenks', popup.vars = c('Geo_NAME', 'Total Population', 'Percent Population 65 and Above', 'Primary Care Physicians per 100K', 'Percent Without Insurance (Under 65)', 'Confirmed Cases', 'Confirmed Cases per 100K')) +
  tm_layout(title = "Per Capita COVID-19 Confirmed Cases By County",
    title.size = 1,
    title.position = c("center", "top"))

# tm_format

cty_map

# tmap_save(cty_map, filename = 'county_map_test_v2.html')

# cty_layers <- tm_shape(shape_reproj) +
#   tm_polygons(c('Confirmed Cases per 100K', 'Primary Care Physicians per 100K'),
#               style = 'jenks', 
#               popup.vars = c('Geo_NAME', 'Total Population', 'Percent Population 65 and Above', 'Primary Care Physicians per 100K', 'Percent Without Insurance (Under 65)', 'Confirmed Cases', 'Confirmed Cases per 100K'),
#               title = c('Confirmed Cases per 100K', 'Primary Care Physicians per 100K')) +
#   tm_facets(as.layers = TRUE)
#   # tm_layout(title = "Per Capita COVID-19 Confirmed Cases By County",
#   #   title.size = 1,
#   #   title.position = c("center", "top"))
# 
# cty_layers